{"id":"W7143766186","doi":"10.15083/0002008347","title":"ロングリードシーケンサーのシミュレーション","year":2022,"lang":"en","type":"dissertation","venue":"Institutional Repositories DataBase (IRDB)","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics","keywords":"Process (computing); Identification (biology); Product (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002026908,0.0004863453,0.0002675015,0.001322482,0.002621199,0.01090882,0.000918779,0.001462528,0.1021399],"category_scores_gemma":[0.006850214,0.0002976762,0.0004017866,0.001880934,0.002231671,0.007933043,0.002101045,0.001779563,0.04030602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002705127,"about_ca_system_score_gemma":0.004340288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008376658,"about_ca_topic_score_gemma":0.006850451,"domain_scores_codex":[0.998147,0.0004575651,0.000108301,0.0004197845,0.0006355996,0.0002316922],"domain_scores_gemma":[0.9974819,0.0005099027,0.0001768789,0.0003658699,0.001205885,0.0002595916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001157831,0.00008028783,0.004472754,0.0002826111,0.00002947715,0.0002195082,0.005880076,0.0004611006,0.001143423,0.6142137,0.1507089,0.2223924],"study_design_scores_gemma":[0.00002015062,0.00003037891,0.003339666,0.000221981,0.00002141455,0.0002285023,0.004426689,0.0003225972,0.0008318078,0.07711151,0.913419,0.00002615851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.01130658,0.001912532,0.01147265,0.01211926,0.00164374,0.0001254915,0.0009508314,0.0003681491,0.9601007],"genre_scores_gemma":[0.2892804,0.004889138,0.01872243,0.006673269,0.001160862,0.0003854107,0.002270798,0.000520273,0.6760975],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1021399,"threshold_uncertainty_score":0.3416919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00845713578055361,"score_gpt":0.2358517905036925,"score_spread":0.2273946547231389,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}